
from __future__ import annotations

import math
from collections.abc import Callable


def 		UpperCAmelCase     (      a_						,  a_						,  a_						,  a_ = 1_0_0						,  )						->      float:







				"""simple docstring"""



				__A       =					x_start
				__A       =					fnc(a_		)
				__A       =					0.0

				for _ in range(a_		):
								# Approximates curve as a sequence of linear lines and sums their length
								__A       =					(x_end - x_start) / steps + xa
								__A       =					fnc(a_		)
								length += math.hypot(xa - xa						,  fxa - fxa		)

								# Increment step
								__A       =					xa
								__A       =					fxa

				return length


if __name__ == "__main__":


					def 		UpperCAmelCase     (      a_		)						->      Optional[Any]:







									"""simple docstring"""



									return math.sin(1_0 * x		)

					print('f(x) = sin(10 * x)')
					print('The length of the curve from x = -10 to x = 10 is:')
					SCREAMING_SNAKE_CASE				:Union[str, Any]									=							10
					while i <= 10_0000:
										print(f'''With {i} steps: {line_length(f, -10, 10, i)}''')
										i *= 10






'''simple docstring'''


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, TensorType

UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """imagegpt"""
   _a    						=   ["""past_key_values"""]
   _a    						=   {
       """hidden_size""": """n_embd""",
       """max_position_embeddings""": """n_positions""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }







   def __init__(						self   ,				UpperCamelCase=512 + 1   ,				UpperCamelCase=32 * 32   ,				UpperCamelCase=512   ,				UpperCamelCase=24   ,				UpperCamelCase=8   ,				UpperCamelCase=None   ,				UpperCamelCase="quick_gelu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=1e-5   ,				UpperCamelCase=0.02   ,				UpperCamelCase=True   ,				UpperCamelCase=True   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				**UpperCamelCase   ,				)					->     int:
     __a								=       vocab_size
     __a								=       n_positions
     __a								=       n_embd
     __a								=       n_layer
     __a								=       n_head
     __a								=       n_inner
     __a								=       activation_function
     __a								=       resid_pdrop
     __a								=       embd_pdrop
     __a								=       attn_pdrop
     __a								=       layer_norm_epsilon
     __a								=       initializer_range
     __a								=       scale_attn_weights
     __a								=       use_cache
     __a								=       scale_attn_by_inverse_layer_idx
     __a								=       reorder_and_upcast_attn
     __a								=       tie_word_embeddings

     super().__init__(tie_word_embeddings=UpperCamelCase   ,				**UpperCamelCase   )





class  __lowercase    (	__magic_name__			):







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('input_ids', {0: 'batch', 1: 'sequence'}),
         ]   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = 1   ,				UpperCamelCase = -1   ,				UpperCamelCase = False   ,				UpperCamelCase = None   ,				UpperCamelCase = 3   ,				UpperCamelCase = 32   ,				UpperCamelCase = 32   ,				)					->     Mapping[str, Any]:
     __a								=       self._generate_dummy_images(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
     __a								=       dict(preprocessor(images=UpperCamelCase   ,				return_tensors=UpperCamelCase   )   )

     return inputs



